Yamal and Polar Urals: a research update

Records of tree-ring characteristics such as their width (TRW) and density (usually the maximum density of the wood formed towards the end of the growing season – the “maximum latewood density” – MXD) are widely used to infer past variations in climate over recent centuries and even millennia. Chronologies developed from sites near to the elevational or latitudinal tree lines often show sensitivity to summer temperature and, because of their annual resolution, absolute dating and relatively widespread nature, they have contributed to many local, continental and hemispheric temperature reconstructions. However, tree growth is a complex biological process that is subject to a range of changing environmental influences, not just summer temperature, and so replication, coherence and consistency across records and other proxies are an important check on the results.

Tree-ring records have greater replication (both within a site and between nearby sites) than other types of climate proxy. Good replication helps to minimise the influence of random localised factors when extracting the common signal, and it also allows the comparison of information obtained from different independent sets or sub-sets of data. If independent sets of data – perhaps trees with different mean growth rates or from different sites – show similar variations, then we can have greater confidence that those variations are linked to real variations in climate.

In a new QSR paper (Briffa et al., 2013), (BEA13) we have used these approaches to re-assess the combined tree-ring evidence from the Yamal and Polar Urals region (Yamalia) of northern Siberia, considering the common signal in tree-growth changes at different sites and in subsets of data defined in other ways. Together with our Russian colleagues and co-authors, we have incorporated many new tree-ring data, to increase the replication and to update the chronology to 2005 and have reassessed the inferences about summer temperature change that can be drawn from these data. The paper is published as an open-access paper (no paywall) and supplementary information including the raw tree-ring and instrumental temperature data are available from our website.

Figure 1 illustrates our inferences about past summer temperature variations. Low tree-growth periods for which the inferred summer temperatures are approximately 2.5°C below the 1961-90 reference are apparent in the 15-year smoothed reconstructions (Figure 1d), centred around 1005, 1300 (Figure 1b), 1455 (Figure 1c), 1530, particularly the 1810s where the inferred cooling reaches -4 or even -6°C for individual years (Figure 1a), and the 1880s. These temperature estimates will be interesting for the current debate about the representation of volcanically-induced cooling in temperature reconstructions, and for testing of climate model simulations.

There are numerous periods (Figure 1d) of one or two decades with relatively high growth (and inferred summer temperatures close to the 1961-90 level) but at longer timescales (Figures 1e and 1f) only the 40-year period centred at 250 CE appears comparable with 20th century warmth. This early warm period was both preceded and followed by periods of low ring width and so the central estimates of the temperature reconstruction averaged over the warmest 100-year period near the 3rd century CE (205-304 CE) are 0.4°C cooler than the 1906-2005 mean. Allowing for chronology and reconstruction uncertainty, we find that the mean of the last 100 years of the reconstruction is likely warmer than any century in the last 2000 years in this region.

Figure 1 (from Fig. 13 of BEA13). Summer temperature reconstructions based on either the Yamal ring-width chronology (red line, orange confidence intervals) or by combining information from the Yamal and Polar Urals ring-width chronologies and the Polar Urals density chronology (blue line, blue confidence intervals). The latter is shorter because the Polar Urals data are shorter and also has two versions that differ in how they are calibrated and in the summer temperature that they represent (in panels (a)-(e) it represents mean June–August temperature shown by the black dotted lines, while in panel (f) it represents mean June–July temperature shown by black continuous lines). Each panel shows a different time period and degree of smoothing; the values near to the end of the smoothed series are more uncertain than shown here due to the presence of end effects on the spline filters. The low-frequency agreement between the series is expected because the Yamal ring-width data are common to both reconstructions.

A response to the critics

The publication of our paper provides a timely opportunity to revisit and respond to a series of unfounded criticisms that have been levelled at our work in recent years, mostly originating from Steve McIntyre at the ClimateAudit blog, though they have been widely repeated and embellished by other commentators.

It is of course usual for results to be improved and superseded as science progresses. Our new Yamalia ring-width chronology differs from the Yamal chronology published by Briffa (2000) – see Figure 2a for a comparison. The very recent values are now lower (and extend by a decade more), but so are the estimates around 1000 CE. The consequent differential between medieval and modern growth is hardly changed. The period of high growth centred near to 250 CE (noted above) is also relatively unchanged, and is now the most prominent pre-20th century period of anomalous growth in the last 2000 years. These changes are because of genuine scientific progress, not because – as our critics have claimed – we had previously presented a deceptive chronology. They arise from extra data collection and, particularly, developments in tree-ring standardization methods (see the paper for details).

Figure 2. (a) Comparison of the Briffa (2000) Yamal ring-width chronology (red) and the new Yamalia ring-width chronology (black). (b) Comparison of the new Yamalia ring-width chronology (black) and two chronologies that have been promoted by critics of our work, but which turn out to be biased: the Polar Urals “update” chronology (purple; from Esper et al., 2002) and the Yamal chronology with modern data coming only from the Khadyta River site (blue). All series were scaled to have unit variance before being smoothed with a 10-year filter.

Figure 2b compares the new Yamalia chronology with two alternative chronologies heavily promoted by McIntyre and others – the so-called Polar Urals “update” chronology and a Yamal chronology using modern samples from the Khadyta River site. Both chronologies present a different picture of the difference between peak medieval and peak modern growth rates, with elevated growth around 1000 CE and suppressed growth in the 20th century. Our paper demonstrates that these two alternative chronologies are flawed.

The real Yamal deception

Some background is perhaps needed regarding our preferred chronologies. Briffa et al. (1995) developed chronologies from Polar Urals ring width and density data. Subsequently, Briffa (2000) presented a 2000-year ring width chronology from nearby Yamal, which had much better replication (more trees) than the Polar Urals data and was therefore preferred. The Polar Urals data were later supplemented by additional samples which were used by Esper et al. (2002). Even including these additional samples the Yamal chronology remained better replicated: of the 1213 overlap years, the Briffa (2000) Yamal has 4 years with samples from less than 10 trees, while the “updated” Polar Urals chronology has 264 years with data from less than 10 trees, many of them in the medieval period (see here for more details). The additional sub-fossil data used in our new paper further increases the replication of the Yamal chronology compared with the Polar Urals chronology (Figure EC1 in the SI of the new paper). On the basis of replication and the strength of the common signal, the Yamal record was, and remains, superior to the Polar Urals chronology.

1: Why we didn’t use the Polar Urals “update”

We have been criticised for not archiving the Polar Urals “update” data. The “update” data were in fact archived at the ITRDB thirteen years ago. We have been criticised for not publishing an updated Polar Urals chronology using the updated data (and accused of worse here). The supposed reason for our decision not to do this was that the ‘update’ does not support our supposedly desired message of unprecedented modern warmth (because they appear to suggest that tree growth rate was greater during earlier times including the medieval period; Figure 2b, compare purple and black lines).

However, as reported in BEA13, it turns out that during the medieval period these Polar Urals “updates” are dominated by samples taken from the root collars of trees. Ring widths measured in such root-collar samples tend to be systematically larger than equivalent rings measured higher in the boles (stems) of the same trees. The reason for larger tree-ring widths during medieval times in the Polar Urals “updates” is now clear: it is because more samples were from the root collar with their inherently wider rings. Interpreting this as evidence for warmer temperatures is wrong.

Conclusion: the so-called “Polar Urals update” chronology is severely biased and should not be used as evidence of past changes in temperature; nor should our critics present it as evidence that we had committed scientific fraud by failing to publish a chronology using these data.

2: The Yamal record was not biased by omitting data

CRU has been accused of deception by presenting a Yamal tree-ring chronology biased by the omission of otherwise suitable data. A particular theme, originating again from ClimateAudit, is that tree-ring data from Khadyta River had not been used and would have dramatically altered the character of the chronology – and the NH temperature reconstructions that used the Yamal chronology – if these data had been used (Figure 2b, compare blue and black lines).

As reported in BEA13, through collaboration with our Russian colleagues who have extensive knowledge of tree-rings in this region, we have learnt that the Khadyta River site has problems related to the particular site conditions that differ from other sites in this region, and maybe influenced by changing permafrost. Certainly the trees have reduced growth and appear to be unhealthy, and some even dying. Thus the Khadyta River data that some claimed as being more representative than the data we used turn out to have a common signal that is inconsistent with the majority of site chronologies in this region. They could potentially bias the Yamal chronology had they been included and so for this reason we excluded these data from the main analysis in the new paper.

Conclusion: claims of a deceptive and biased Yamal chronology turn out to rely on outlier data that should be omitted; our new research, based on a greatly expanded dataset, supports the finding that tree-growth (and inferred summer temperature) in this region are likely greater in the last 100 years than for any previous century in the last 2000 years.

3: We did not withhold a combined Yamal and Polar Urals chronology

Separately, some of our incomplete and unpublished work on the Yamal and Polar Urals tree-ring data has been the subject of multiple requests under UK FOI/EIR legislation. (See this previous post for background). To be clear, this was not a request for the raw data that we were using in this area of northern Russia – the raw data were and are freely available. Instead, the request was for a tree-ring chronology that formed part of work that was, at the time, still ongoing.

The EIR has a (very sensible) exemption for material which is unfinished, incomplete or still in the course of completion. Our university (UEA) therefore refused the requests to release our incomplete research (see responses here and here). Steve McIntyre appealed and UEA reconsidered the issues but upheld the original decision. McIntyre then complained to the Information Commissioner’s Office (ICO). The ICO upheld UEA’s decision and rejected McIntyre’s complaint. McIntyre then appealed to the First-Tier Information Tribunal. Two weeks ago, after more than two years defending our right to publish our research at a time when we considered it to be complete rather than at a time dictated to us by Steve McIntyre, the Information Tribunal finally dismissed McIntyre’s appeal.

The research that was the subject of this information request has now been – as we said all along that it would be – completed and published, coincidentally, within days of the Information Tribunal’s decision. Our publication of this work contradicts McIntyre’s explicit accusations that we were hiding the requested chronology because it would have exposed long-standing scientific fraud on our part. These accusations were, and remain, baseless and mistaken.

Over the years, McIntyre has advanced a number of other criticisms of our tree-ring work in northwestern Eurasia. We note here that these too are also wrong.: 1) the original Polar Urals chronology was not wrongly cross-dated as claimed in a 2005 submission to Nature by McIntyre and McKitrick. When we demonstrated this in our response, Nature decided to publish neither their comment nor our response. It is worth noting that this rejection, nor any acknowledgement of his erroneous conclusions, were ever mentioned by McIntyre on his blog. (2) The Grudd (2008) Tornetrask chronology, promoted by some because of its elevated medieval growth (and implied much greater warmth) relative to the modern period, is biased by the issues noted in Melvin et al. (2013).

In conclusion, criticisms of our work have been based on misconceptions and misinformation. The so-called Polar Urals “update” chronology promoted by our critics turns out to be biased by inclusion of samples from tree root collars. The Khadyta River tree-ring data, whose exclusion from the Yamal chronology was portrayed as a severe example of cherry-picking to obtain a pre-conceived outcome, are from trees that appear to be dying and do not have a common signal with other regions. An updated Tornetrask chronology, with apparently elevated medieval warmth, turns out to be biased by combining incompatible groups of measurements.

That the critics have promoted a series of results that have turned out to be flawed is unfortunate but not in itself reason to complain – as science progresses it is usual for results to be improved and superseded. What can be condemned, however, is the long campaign of allegations of dishonesty and scientific fraud made against us on the basis of these false claims. That is the most disquieting legacy of Steve McIntyre and ClimateAudit. The real Yamal deception is their attempt to damage public confidence in science by making speculative and scandalous claims about the actions and motivations of scientists while cloaking them in a pretense of advancing scientific knowledge.

111 Responses to “Yamal and Polar Urals: a research update”

Well done Tim, Tom and Keith and thank you for your courage and determination. The story of the attacks you have been forced to endure and your response to them is an important one to tell and needs to be known widely by scientists, politicians, and the public.

[Response: Fixed the figure, but
I don’t see the problem with the doi. The in-text link points to the references which have the right doi – and indeed it has to be right since the ref is automatically generated from the doi. Let me know if I missed something. – gavin]

Aren’t the conclusions of 1 and 2 post hoc explanations for exclusion of data? Was the excluded data and reasons for exclusion stated in the original papers? Post hoc data exclusion of data was a big problem in medical research and was in part one of the reasons for the development of the CONSORT statement.

fred smith: “Aren’t the conclusions of 1 and 2 post hoc explanations for exclusion of data?”

No.

The fact that some Polar Urals cores were taken from root collars was noted at the time the samples were taken — please see figures PU05 and PU06 of SM3 at our supplementary materials webpage:http://www.cru.uea.ac.uk/cru/papers/briffa2013qsr/
Once aware of this information, the decision to exclude the root collar samples was taken on the the basis of the likely incompatibility of the ring widths from root versus trunk samples. We did attempt an adjustment to compensate for different mean ring widths between root versus trunk samples, so that the root data could be retained and used, but without success due to the added complication of difference growth trends as well as mean growth rates. These attempts are documented in SM4. For tree density (MXD), such an approach did work and we could therefore use the MXD data even from the root samples.

For the second case, the potential problems with the Khadyta River site were also noted at the time the samples were taken — please see SM2 (pages 1, and 5 to 8). Also note that we have previously demonstrated that inclusion of these data would have a relatively small impact anyway, something that we updated in Figure 10 of our new paper. Panels (b) and (e) include Khadyta River data, while the other panels do not (note that there are data and method differences between the panels too, which make larger differences to the final chronology).

1. Is this “root collar” problem in any way similar to the “strip-bark” problem (other than the idea that “strip bark” may be declared a non-issue on the basis that it agrees with what we think the other trees are basically saying (from Salzer et al)? Meaning, if the “root collar” issue occurred in samples that were registering similar results after analysis they would be still declared acceptable for dendroclimatological use?

2. I always get it in bits and pieces from the various places that discuss climate, but to date I haven’t found a self-contained explanation as to why studies in Dendroclimatology are not on the same plane as medcal/phar research, and in effect “unique” in that they can pick and choose samples (to paraphrase Esper). For as common a signal as the trees are representing, there still are surprisingly few trees in the mix compared to the number of trees in the world (and at these prior-chosen sites).

3. From what I’m reading here, the indicated research/enlightenment into discounting the Khyadta came after the determination of what its data showed, not before. Is this true?

Much of the criticism could have been neatly sidestepped by transparently releasing the tree-ring chronology when first requested, instead of sitting on it for two years while (admirably) continuing its development, but “in the dark” so to speak.

There is much to be said, in a positive way, for total transparency and admitting the light and expertise of other researchers. It is unfortunate that UEA chose not to.

[Response: Sorry but that’s rather naïve. Releasing our incomplete and unpublished work, specifically a chronology that we consider to be biased, before we had time to complete our evaluation and provide the written explanation about why it was biased, would not have avoided any of the manufactured controversy. Recall also that the existence of this work-in-progress chronology was only “public” because of its mention in some of our hacked emails. Taking your suggestion to its logical conclusion would mean that everything we do should be public while we work on it. The UK EIR/FOI exempts incomplete information for a good reason, and UEA applied this exemption in good faith.- Tim Osborn]

First, note that root collar samples are acceptable to use in some circumstances. Take a look at Figure 5a of our main paper: at timescales below 100 years, there’s agreement between the updated Polar Urals (with many root samples) and the original sub-fossil Polar Urals data (with few root samples). It is on longer timescales that the different growth behaviours are apparent (Figure 5b, 5d).

Ideally dendroclimatologists would like to avoid using such samples (if their interest is in the longer timescales). But if the sample count is low, then comparison with other trees may be a reasonable approach to deciding whether non-ideal samples could be used to increase the replication. We cover this quite a bit in our paper — looking for common behaviour between independent sets of data as one measure of confidence (Figure 5f shows the high standard deviation 900-1100 and 1400-1600 when roots are included, and thus lower confidence in the chronology mean value).

Fritz Schweingruber was aware that the replication of the original Polar Urals chronology was quite low during medieval times and asked for further samples (see page 4 of our SM4 document), which turned out to be non-ideal, as we’ve demonstrated by comparison with other trees but which might have been expected because they are from the root collar. Note that Fritz was particularly interested in the MXD values, which are much less affected. Our approach was to go with the better replicated Yamal TRW chronology instead.

Does anybody still read McIntyre? I thought his complete lack of reasonableness had put him into the cyber dustbin. I used to come here and look there but I haven’t looked at his blog in several years. I actually thought he had packed it in.

1) Thanks for the good science. Some of these examples show thoughtful analysis of data rather than blind statistics that just includes outliers. In many fields, some outliers may be real and need to be included, but all too often, something is truly wrong with part of the data and it needs to be discarded. Of course, some people then claim it was post hoc discarding data you didn’t like, but I’d say your choices are well reasoned. The most famous recent analogy could be the “faster-than-light neutrinos” caused by a loose cable.

2) Thanks for publicly documenting McIntyre’s extra-science methods.
Hopefully more will do so.

We don’t generally pick and choose samples. I’m not sure of the background behind Jan Esper’s comment, but did he mean that you can sample from moisture-stressed sites if you want to try reconstructing precipitation/drought and you can alternatively sample from temperature-limited sites if you want temperature?

Our approach was to begin with all samples unless there was a reason not to (e.g. root collar samples), but then to consider common signal between independent subsets to identify any issues. None of our selection was based on correlation or non-correlation with temperature observations. The comparison with temperature came only after we had finalised the chronology.

So it is an unfair characterization to say that we just pick and choose samples.

Not true. Our initial plan was to include the Khadyta River data in the main chronology for our new paper — even after we had inspected them and seen that they behaved differently to data from other sites in the region. Subsequently our Russian colleagues pointed out that there were issues with this particular site, that the trees were not considered ideal for dendroclimatic analysis and were not healthy. That was the basis of their exclusion from the final presented chronology (though they are in some panels of Figure 10 for comparison; see my earlier comment).

The two links provided still appear to be post-hoc explanations as fred#3 suggests. I think the criticisms were related to Briffa 2000, and Briffa et al 2008?

These links are for post-hoc disclosure in 2009 and 2013? Is there any earlier disclosure so far not referenced?

[Response: The Polar Urals update (dominated by root collar samples in the medieval period) and Khadyta River data were not considered in Briffa (2000). Therefore there was no decision to include or exclude them, and thus neither an a priori nor a post hoc justification is relevant. They weren’t considered in Briffa et al. (2008) either, though we had begun some preliminary comparisons of different sites within the Yamal region and identified some potential problems in combining data from multiple sites. Given these problems in using multiple sites, Briffa et al. (2008) instead chose the single site with the best replication — Yamal. -Tim Osborn]

Thanks a lot for this very informative post, for the great science in your new paper and for not letting ‘them’ get to you.
And thanks for your long term contribution to expand the human knowledge.
With best regards, Jos

On his blog, Dr Bouldin has made a series of convincing posts on severe analytical problems in dendroclimatology. From the account of the paper given above, it would seem that it falls into many, if not all, of the traps diagnosed by Dr Bouldin. Would anyone be prepared to comment on that?

Thank you for this post, it clears up a number of questions. Forgive my ignorance, perhaps you can shed some additional light one item. The discussion of root collar vs tree bole samples includes the statement “it is because more samples were from the root collar with their inherently wider rings”. I would have thought the comparison across sites and even different sample locations within the same tree would be relative ring width rather than absolute ring width. Trees in more favorable micro-environments, or species that tend to achieve higher diameters on the same site, would have wider rings. Leaning or wind exposed trees develop difffernt wood on compression and tension sides of the bole. I would have thought a standard process in dendroclimatology would be to normalize based on relative difference within the same sample so absolute width would not be an issue. It seems odd that the wider tree collar widths were not accounted for initially. Am I misinterpreting the root collar discussion? Thanks for any assistance you can provide.

I have no direct experience with this work, but I am familiar with issues of data stewardship and custodianship, primarily in a business context, but also elsewhere. Releasing data which has not been well-vetted can be a source of harm, since many people will use such data without question, and are wildly uncritical, leading them to incorrect conclusions. Sometimes, as in Internet measurements research, these results, in turn, make it into the conference literature, which leads still others to believe the work is well-founded. The “random networks” subfield of Internet modeling was one such example.

The U.S. Geological Survey qualifies map data according to the level at which it has been checked and corroborated, such as their Digital Elevation Maps. They use a Likert scale of 1-4 to indicate this. While such an approach may be okay for a standard product like a digital map, it’s more difficult for complicated data sets in general scientific work.

Thus, I applaud efforts at being careful regarding release of data, and of complete scientific datasets, especially in an environment where transparency is being enforced by policy.

It’s an interesting statistical question whether or not there might be a scoring procedure devised which assessed consistency of a given datum with its neighbors. Such a score might be in the form of a Likelihood value, namely, L(data|model), but that begs the question of what to use for “model”. I have not examined whether Owen’s work on empirical likelihood may apply here, but that’s where I would go with the question if I were to pursue it (\cite{A.B.Owen, EMPIRICAL LIKELIHOOD, Chapman & Hall, 2001}).

The issue of root collar samples having wider rings can’t be dealt with by using relative ring-widths because that would remove all long timescale variability.

Using “relative ring widths” would mean dividing the measured ring widths by the mean ring width (or mean growth rate) for that tree core. The mean of the relative ring widths for that tree core would then be equal to one. Cores from a tree growing, say, during 1000-1300 and from one growing during 1700-2000 would have the same mean relative ring-width regardless of whether the climate was more or less favourable to tree growth in each period.

This is the problem with what are often called “curve-fitting” standardisation methods — they remove differences in mean growth rate between tree cores even if the difference arises from climate changes that we are trying to reconstruct. (In fact they also remove the trend over the course of the tree’s life as well.) Thus tree-ring chronologies developed using curve-fitting standardisation will have little variability on timescales longer than the typical length of a tree-core sample — the “segment-length curse” described by Cook, Briffa, etc.

The RCS standardisation approach retains differences in the absolute growth rates of different tree cores. Thus it can retain evidence for long timescale variability. But it is also more sensitive to issues such as those you raise. Some of these other influences on ring growth may be random and therefore can cancel out with a larger sample — one of the reasons why a larger sample is needed for RCS. Some may be more systematic and various approaches can be used to address this. In our paper we grouped tree cores into faster and slower growing trees (which might reflect some of the micro-environmental factors you mention) and processed them separately. See Figure 3b and 3c of our paper — the red and blue curves contain independent data, yet note the agreement between them.

You also mention different growth rates between species — here we have used only Larch (Larix sibirica) to avoid that issue.

Otoh, maybe you guys owe Stevie McInt a beer. If he hadn’t spent his time inventing alternate climate recon-fictions, and badgering for a right to stand over your shoulder critiquing your WIP … you wouldn’t have produced this obvious quality piece of research. And he might have made someone else’s life miserable.
Guelph’s legacy was a home for retired Mafia members, HQ for a popular local brewery that sold out, site of a university furnace that’s been used for burning pot & hash seizures … and now Steve McIntyre. Go ahead, claim it’s just co-incidence.

I am enjoying this dialogue, as dendroclimatology has somewhat of a unique position within science and statistics that permits a deeper exploration of both fields with each publication.

To conitnue… when you said:

“Our initial plan was to include the Khadyta River data in the main chronology for our new paper — even after we had inspected them and seen that they behaved differently to data from other sites in the region. Subsequently our Russian colleagues pointed out that there were issues with this particular site, that the trees were not considered ideal for dendroclimatic analysis and were not healthy.”

I was immediately wondering (a) who would core unhealthy trees if this is a disqualifying factor at the outset? (b) couldn’t presently unhealthy trees still be informative in a healthier state long ago? ( c) who’s to say that presently valued cores are from still healthy trees? It’s amazing that these trees are geotagged with such specificity that anyone can go back and pay a visit to the exact specimen later and re-core it if necessary.

I’m still a little hung-up on the phrases ‘non-ideal’ and ‘replication’ … Some samples can be acceptable and others rejectable if they are ‘non-ideal’, but it appears the basis is on the degree of ‘replication’… Is that correct? The idea “better replicated” data is ‘went with’ more often, and the less replicating data being more mined for exclusionable grounds. However, I do understand that if the original purveyor of the data declares a set as useful and another as not, you would presumably have to go with their diagnosis lest there arise a contradiction/conflict.

Oh, and by the way, the quote attributed to Esper is from Esper et al (2003) where it is said,

[one could conceivably improve a chronology by reducing the number of series used]”…if the purpose of removing samples is to enhance a desired signal. The ability to pick and choose which samples to use is an advantage unique to to dendroclimatology” – hence my med/phar comparison question. I sure would bet that those researchers would love to be able to similarly declare such a uniqueness for their products.

[Response: We can’t speak for Fritz Schweingruber (who cored the Khadyta River trees) or Jan Esper (re. the quote from Esper et al., 2003), though we offer the following:
_
(a) if trees are patently ‘unhealthy’, wouldn’t generally sample them, unless one is interested in identifying the timing (and possibly cause) of the condition.
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(b) yes, presumably so – but in interpreting the ‘signal’ represented by their changing ring widths, one would have to explicitly recognise and ideally take account of the anomalous signal they would present when “unwell”.
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re. geotagging: many trees are not precisely locatable, but returning to a proximal region should be sufficient to identify a coherent regional growth signal.
_
re. replication: note that Tim used this in his earlier comment to indicate simply the sample size available in any one year. “Better replicated” meant based on more samples, rather than implying anything about the strength of the agreement between samples. “Better sampled” or “more highly replicated” might have caused less confusion.
_
There are several concepts entwined in your comment, relevant to the underlying rationale of chronology construction and working approaches. We say “approaches” because the particular techniques in chronology building and assessment of chronology confidence will depend on the specific questions being asked. If the aim is to reconstruct the short-timescale (say interannual to decadal) variability of tree-growth it makes sense to remove the unwanted variability at source – say by high-pass filtering the measurements before averaging the series to produce a chronology. The averaging then efficiently cancels “noise” (i.e. variance which is not commonly represented in the high-pass filtered series) and the statistical confidence of the chronology can be accurately assessed by comparing the magnitude of signal (common variance) to residual noise. However, if we are interested specifically in the long-term common growth variability of a group of trees, this is harder to isolate and quantify in terms of chronology confidence.
_
Tree-ring measurement series contain long-timescale variability that is not forced by climate but by systematic thinning of radial growth rings in older and larger trees. This thinning process must be modelled and the effect removed. Slight errors in this process and the generally weaker representation of common long-timescale variability expressed in groups of trees mean that low-frequency chronology variability is not as strongly expressed as the high-frequency given the same number of tree core samples. This problem is reduced if a very large number of samples are available, but when this is not the case, rather than rely on the averaging process to cancel non-common growth signals it could be argued that it is more efficient to remove clearly “anomalous” data from the sample. Provided “clearly anomalous” is defined with regard to the underlying pattern of common growth variability and not with regard to some “desired” target, such as the observed temperature trend.
_
In the case of the Khadyta River data, one might argue that it is better practise to leave the data in and depend on the averaging process to produce the “best estimate” of the underlying regional tree-growth signal. Without explicit knowledge that the apparent health of these trees was sub-optimal, we would probably agree with this logic. However, given the additional knowledge that the trees are unhealthy and the site not considered ideal for dendroclimatic analysis, it is reasonable in this case to exclude these data from the regional chronology. As it happens, the chronology including these data is not significantly different from our final chronology.
-Keith Briffa, Tim Osborn, Tom Melvin]

I was recently pointed to this topic by a skeptic who suggested I Google “most influential tree in the world”. Your post and others are good responses to McIntyre but I’ve never seen a response to that particular point anywhere. I do not believe that 80% of the postulated warming is due to one tree, an 8-sigma outlier called YAD06, for the simple reason that you and your reviewers are not total idiots, but it would be nice to have something to point to to show that McIntyre is a total idiot on this point.

I appreciate the huge effort you have clearly put into this “definitive” work and admire the fact that you have also made the data available. It is obvious though that the spotight will now fall on whether or not the data show an anthropogenic signal during the 20th century. I have downloaded your combined “Yamalia” data and would agree that they show a 20th century warming signal of about 1C, noting however that a similar magnitude excursion occurred ~ 300 AD. However, my main question concerns the 15 year and which 100 year “smoothing” algorithms you used since I am unable to reproduce them. I suspect this 100y smoothed graph will be the one most likely picked up by the media. You yourselves state under Fig 13: “Note that the smoothed values at the ends of the series are much more uncertain due to the presence of end effects on the spline filters, especially for (e–f).” so this could back-fire.

I’m commenting on the paper relative to those more fundamental issues, as time permits. The Yamal data is definitely better than most data sets, at least relative to RCS detrending concerns (as is that of Esper et al., 2012), and also better than the Polar Urals, but other critical concerns remain (as they do for virtually all tree ring studies attempting to estimate relative climatic state variables over centuries). See:http://ecologicallyoriented.wordpress.com/2013/06/08/briffa-et-al-2013-part-two/

One more aggravating comment on the left-out Khadytla sample, purely from methodological curiosity. It is clearly okay to discard an anomalous outlier when studying 20th century climate. But once you compare the 20th century to the middle ages, I think one would need to leave in the outliers. There might well be trees in your medieval sample that represent a local, anomalous, non-climate signal, too. (You can’t really know, can you? If you are able to spot and discard medieval problem trees, forget my comment) When you discard 20th-century non-climate signals, then compare with a medieval sample where non-climate-variation isn’t excluded, you will probably get an enhanced climate signal in the 20th century. Won’t you?

Your point is a good one on a couple of levels. We can quantitatively know the relationship between environmental driver and ring response only during the calibration period, when we have both types of data. In the pre-calibration period, the response to the environment has to be inferred from correlations between the growth responses of different trees, relative to expectation under a null model of randomness. It would be an interesting study, IMO, to examine the intra-site coherence in ring response (across some set of sites), to see how that coherence changes between the pre-calibration and calibration periods.

Your point also relates to calibration itself. If we calibrate a driver/response relationship based on a criterion of some minimal correlation (or probability) from a linear model, but the calibration period from which that derives only actually samples some part of a more complex, non-linear response surface/curve, then the estimates of the parameter of interest in times past could be seriously wrong and/or the certainty in the parameter over-estimated. And that’s a major consideration and potential problem.

Thanks for the comments over the weekend… we’ll answer/respond as time allows. Here’s the first.

Response to Clive Best:

Thanks for your comments.

Yes, we highlighted the high-growth (and inferred warm summers) anomaly around 250 AD in our abstract and elsewhere in the paper. There are many interesting features to study, in addition to the modern warming.

The smoothing was done using a spline approach. The algorithm/code for that was published in Melvin et al. (2007, Dendrochronologia, doi: 10.1016/j.dendro.2007.01.004) – see Appendix A for the computer code.

We drew attention to the issue of end effects of the spline. Comparing the relative levels of different periods should only be done using means of the unfiltered data to avoid end-effects from influencing the outcome. Our comparisons of recent values versus earlier reconstructed values (as reported in, e.g. our abstract, Table 1, and Sections 9, 11, and 12) all used the means of the unfiltered data for this reason.

The decision to exclude the Khadyta River Larch (or “khadytla”) sample was not based on comparison with instrumental data and hence not on the basis that it had a non-climate signal. Instead, the decision was based on the TRW data themselves:

(1) evidence that the behaviour of this sample/site was different to the behaviour of the other samples/sites considered (see Fig. YT07 and YT06 in our Supplementary Material SM2.pdf); and

(2) evaluation of site and recommendation from the scientists who have visited this site.

It is the two things together that were important for our decision. Neither of these require instrumental data. But can these two conditions also be evaluated for the pre-20th century data?

The first can be done fairly easily – as Jim suggests, we can look at coherence between tree-ring data over time. We have done this extensively in our paper and in the supplementary information (indeed our chronology confidence ranges are based on the spread from individual values in each year, and become broader when tree indices are in less agreement). Take a look at Fig. 5(f) in our main paper, which shows the changes over time in the standard deviation of the Polar Urals tree-ring widths – the periods with the root-collar samples stand out clearly as having much greater spread between the tree indices. This is the type of analysis that can test whether condition (1) is met in the earlier data, and in the Polar Urals case this was supporting evidence for the removal of the root-collar samples from the tree-ring chronology.

The second is more difficult. For the Polar Urals case, we have the remnant wood. Therefore we do know that some samples were taken from the root collar, with its inherently wider rings, and thus support for our decision to exclude those samples that is separate and independent of the lack of coherence with the other data. But for Khadyta River, the evidence for the living trees being unhealthy and possibly dying came from a site visit and clearly we cannot visit the site 1000 years ago. We do, however, have information about the time-span of the subfossil samples – and if we align these by the year of the last-ring in each sample it can highlight periods of enhanced mortality. See for example Fig. 3(b) of Gunnarson and Linderholm (2002; Holocene, 12, 667-671) for a Swedish chronology. We didn’t include such a plot in our paper, but we have inspected the data in this way and there is no obvious period of enhanced mortality within a group of trees that corresponds with (but partly suppresses) a period of enhanced mean chronology values. That is, there is no obvious pre-20th century equivalent to the modern period Khadyta River case.

Nevertheless, it is worth noting that we did include the Khadyta River data in the chronologies shown in panels (b) and (e) of Fig. 10 in our main paper, which gives an indication of the development of this chronology over time in response to inclusion of extra data and changing methods of processing the data.

Yamal shows an accelerating rise and a gross 3C rise. Yamalia shows a variable but steady rise from the 1800s, with a gross 1.8C rise. Big difference when the difference between AGW and “normal” is slight, and the difference between AGW and CAGW is points of a degree.

Polar Urals and Yamal-Khad are of the same style, quite different from Yamalia. The numbers may be questionable, but the style may still be okay. Yamalia has the same style as Yamal, which is why you are saying the first work was good despite the numerical change. If the Polar Urals and Yamal-Khad style is similarly good to the original Yamal, you are facing a regional style with the Yamal data, not a global or global-representative style.

And the difference from the MWP isn’t that great from today to justify the use of “unprecedented” for today.

Looks to me as if your new work supports a general recovery from the LIA, a continuation of such a recovery with a power equal to, if not the same as, the “natural” processes we all agree brought us out of the LIA. Your new work supports the contention that warming is not accelerating except within the rising portion of any cycle, and that the overall rate of temperature growth is in the low, Scenario B- of the IPCC reports. Which is not supportive of CO2 as demon, leading to catastrophe.

If it weren’t for the political, social and financial costs and implication of your work, you would be feted by all. It is too bad that the energy and struggle you do is used in such a way (with your social group’s enthusiasm)that many wish to toss your baby out with the dirty bathwater.

Do you really want to bet the future of human civilization on a single study of a single proxy–especially when there is a whole helluva lot of science out there that suggests the situation is a whole lot more grave even than that portrayed in the IPCC summaries?

Doug Proctor @34 — The temperature increase following LIA is almost entirely of anthropogenic origin. Orbital forcing indicates that temperatures should have continued to decrease in the absence of human activities.

I’m not sure why the contrarians make such a big fuss over past global warmings. These only serve to support the current warming of today — the fact that there can be global warmings. So even if the MWP were as warm as today, that would only strengthen and support the fact that we are in the midst of global warming; and throw in the warmings of 55 & 251 mya, and we can see that it can get much higher than even today. And who knows how much higher it can get than back then, since the sun is now hotter, and we are causing GW a lot faster.

Now whether past warmings were initiated and/or caused by factors other than GHGs (with GHGs perhaps playing a positive feedback role in great warmings not initiated by excessive GHGs in the atmosphere), in no way negates the fact that GHGs can cause or contribute to global warming (just as many factors can contribute to cancer). Also to my meager knowledge the atmosphere and the warming properties of GHGs do not distinguish between “natural” and industrial-emitted GHGs, responding only to the former with warming, but not to the latter.

The problems with the denialist arguments is that they have more to do with logical fallacies than data (proxy or instrumental). Denialists need to be studying rhetoric and logical fallacies, not clamoring for unpublished data. They need to take their biased microscopes off of the tree-rings (as if they can find some proof in them to disprove the warming of today), and look at the larger picture.

I have question relating to the divergence of tree ring proxy data with instrumental data in the 20th c. (the tree data showing cooler climates). It seems that one aspect of the enhanced greenhouse effect is that the minimum diurnal (night) temps are increasing faster than the maximal diurnal (day) temps (I suppose unlike warmer periods not caused by an enhanced GH effect).

While higher day temps may help plants, it seems these higher night temps have a negative impact on at least some plants, and perhaps other creatures, like humans. I remember the high death toll in the European heatwave of 2003 was more attributed to the high night temps that did not give people a chance to recuperate from the higher day temps.

Anyway, this is was found to have a similar impact on rice paddy in South and SE Asia, and I was wondering if this might also be affecting trees and tree rings.

The differences between Yamal and Yamalia (= Yamal + Polar Urals) aren’t so big — Fig. 1(e),(f) of our main post (which is Fig. 13 of our paper) shows them in red and blue, respectively. Of course this is expected because Yamal TRW is in both! You can see Yamal TRW vs. Polar Urals TRW in Fig. 8 of our main paper (no filtering) and in Fig. PY17 (with filtering) of our Supplementary Material section 5 (i.e. SM5). It isn’t clear to me that they are of different “styles”.

Invoking “recovery from the LIA” is rather woolly. I recall reading work by Akasofu a few years back which postulated a linear 0.5 degC/century “recovery from the LIA” that would last for centuries. What?! Why would that happen, what controls how long the “recovery” takes? Things don’t just “recover”, they respond to forcing factors. And the recovery timescale is linked to the heat capacity of the system, and we can’t pick a long timescale for response to natural forcings (e.g. “recovery from LIA”) but then pick a short timescale when considering the response to more recent anthropogenic forcings. [Doug, I’m not saying that you are suggesting all these things, these are just examples of the woolly thinking that often accompany the suggestion that much of the observed warming can be explained away as “recovery from the LIA”.]

We need to have a more precisely defined framework than that to understand what is going on. What forcings caused the LIA and how did those forcings change since the LIA? How long would the system take to respond to those changes? What part of the observed/reconstructed changes can these natural factors explain? Various studies have used more precisely defined frameworks along these lines (see Fig. 6.14 of IPCC AR4 for example) and (subject to assumptions and uncertainties as always) found that “recovery from the LIA” would have made only a small contribution to the observed warming.

Akasofu’s “recovery from the LIA” might just as well have been “pixie dust.” It sounds much more plausible and ‘scientific’ (and does have a tangential relation to reality, in that there really was an LIA and we really have ‘recovered’ from it)–but without a physical mechanism, it has no more explanatory value than would “pixie dust,” or for that matter, any other label one might choose to apply.

To put it differently, for “recovery from the LIA” to be meaningful, we’d need to have some idea what was causing that recovery–how that recovery ‘works.’ (Then, and only then, we’d be able to generate testable hypotheses about it–like, “Is it done yet?” Or, more immediately testable perhaps, “Is its observed temporal and spatial structure physically consistent with other knowledge?”)

No-one is denying anything. CO2 is a GHG and any increase will (given sufficient time) “force” the climate to reach a new equilibrium. First order physics (assuming a fixed lapse rate) can be shown to result in a net rise in global temperatures of ~1.1C – after a doubling of CO2. The debate is really just about second order effects. In other words how does the rest of the climate system react? Will more evaporation enhance the CO2 greenhouse effect or can more humidity also reduce the lapse rate acting to dampen the GHE ? Does more evaporation lead to more clouds and if so is the net effect of more clouds to increase albedo or to further increase GHE ? I don’t believe any model or any climate scientist really knows the answer. That is why experimental results like this are so important.

Recent results (like this one and Otto et al.) hinting at lower climate sensitivity and reduced feedbacks should be seen as positive developments. Human ingenuity can only work outside the straight-jacket of national or international governments – just look at the Internet! It is a big mistake to legislate now pre-determined technical solutions for restricting CO2 emissions no matter how well-meaning or enlightened the protagonists. There is a problem. It can be solved. We have at least 50 years to solve it. One should never pre-select technological solutions on the basis of transient current political fashion. Solutions will be found and the world will not end.

Clive Best @37: “Human ingenuity can only work outside the straight-jacket of national or international governments – just look at the Internet” You’re joking, right? Or are you truly unaware of the role of DARPA in the creation of the Internet?

HA! If only it were so. Even you, Clive, are denying that there is an immediate, urgent problem in the same comment in which you deny denialism itself.

Were there any point in linking to trash, I could provide immediate, comprehensive examples right now of denialism at every stage: from denial that there’s been warming since 1980(!)–WUWT, yesterday–to denial of human causation, to denial of the potential seriousness of the issue. And there is direct evidence (and volumes of it, at that) that this denialism is supported and nurtured by a deliberate, sustained PR campaign funded by economic and ideological interest groups.

With respect to Mr. Best’s post, which I may be unfairly implying is a good example, one of the fallacious but clever debate manipulations utilized by CC deniers and (way too many) lukewarmers is to focus relentlessly (often inaccurately) on climatological research frontiers such as climate sensitivity, or relations between evaporation, cloudiness, and global albedo.

But that’s just one set of ‘second-order’ effects to be concerned about: from a purely anthropogenic perspective, one should arguably be a lot more concerned about the ‘second-order’ impacts upon estuarine, coastal marine, and freshwater ecologies that are without question unfolding now pretty much everywhere. These cascading transformations throughout the planet’s ecosystems rarely seem to be of much rhetorical concern for the naysayers and ideologues, as evidenced in all those carefully engineered and web distributed denialist PR campaigns and blog comments, and not even here much at RC; although of course the field of dendrology includes the study of how trees are responding to a warming climate.

Why the lack of greater awareness to the intensifying biological and ecological responses to CC beyond scientists and environmentalists? Lots of reasons; but from the perspective of denialism, likely one reason is that the surprising and troubling ecological impacts of AGW and CC that the world’s farmers, fishermen, naturalists, birdwatchers, eco-tourists, and alpinists are observing and experiencing in horror will continue to push denialism into the dustbin of history, only to be held up for cynical ridicule by future generations as they sit and wonder, ‘what the heck were our ancestors thinking?’

YAD061 is not the “most influential tree in the world”. It is a tree with high growth rate and some wide rings, and these contribute to the high values in the original Yamal chronology. But these occur in a period with elevated growth for many of the trees, not just that one tree. So its influence on the Yamal chronology – and on the conclusions drawn from the Yamal chronology – is rather limited. In the Briffa (2000) and Briffa et al. (2008) Yamal chronologies it has only a small influence. In our new chronology, its influence is imperceptible. For multi-proxy reconstructions that use the Yamal chronology along with other proxies, its influence is of course diluted further. And its influence on the climate change issue as a whole is negligible.

Let’s take a closer look at YAD061. In a few years since 1950, this tree had a very high index value of 7 or 8 (meaning these rings are 7 or 8 times wider than would be expected for rings of that age growing in average climate conditions). But this is nowhere near as rare as an 8-sigma value from a Normal distribution, because the TRW index values have strong positive skew (see Fig. PY03 in our SM5) favouring more frequent very high values. It is not the tree with the largest tree index value in the original Briffa (2000) and Briffa et al. (2008) datasets – tree L04551 has larger index values in the 1720s.

There is no clear justification for excluding YAD061, without also excluding other trees with high index values or indeed with low index values – and note the earlier discussion and concerns about post hoc data removal.

However, if you do remove core YAD061 and recreate the old Yamal chronology, the difference is quite limited: see this image. Of course the recent values are lower because you have deliberately searched for and removed the tree with the highest recent index values! But the difference is not enough to affect the main conclusions drawn from that work – clearly not the most influential tree in the world then.

For our new Yamal chronology the inclusion or exclusion of YAD061 makes no perceptible difference to the chronology (see this image; the red line is there, but virtually hidden under the black line). Our conclusions are compatible with those obtained with the old Yamal chronology. So how can YAD06 be the “most influential tree in the world”?!

There are two reasons why YAD061 has no effect on the new chronology and is not an outlier. (1) We have additional data. (2) We have improved tree-ring standardisation processes.

In our new chronology, 17 other trees have peak tree index values that exceed the peak value of YAD061, so it no longer even peaks at the 2nd highest, it peaks at the 18th highest. Of these 18 trees with the highest peak index values, 8 peak values occur in the 20th century and no more than 2 occur in any of the preceding 20 centuries. Clearly the 20th century is a period with enhanced tree-growth, so it is perhaps not surprising to find a tree like YAD061 during this period.

The improved standardisation includes a number of innovations. The key one here is that we now transform the tree index values to follow a normal distribution, which reduces the extremely high index values – e.g. YAD061 peaks around +3.5 standard deviations after this step, compared with the +8 index value before. Together with the expanded dataset, these are the reasons for the lack of sensitivity to inclusion/exclusion of core YAD061. See Fig. 2(a) of this blog to compare “old” and “new” chronologies.

McIntyre overstates the role of this single tree. His post title “YAD06 – the Most Influential Tree in the World” is hyperbole. Maybe he just wants to appear provocative and/or interesting. It has its downsides.

Not least causing others to also overstate things: Booker’s Telegraph piece: “On this astonishing tale, it is no exaggeration to say, could hang in considerable part the future shape of our civilisation.” Really? No exaggeration?

But it can also cause confusion. On 1 March 2010, Lord Nigel Lawson gave evidence to the UK House of Commons Science and Technology Committee that “for a long period before 1421 they relied on one single pine tree” (volume II, evidence EV4, page 9). We don’t know what he meant by this, nor what his source was, and maybe he didn’t really know either – but could he have read a blog post or an article talking about “the most influential tree in the world” and conflated that vague knowledge with questions about tree-ring divergence? It’s possible.